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Group level social media popularity prediction by MRGB and Adam optimization

Author

Listed:
  • Navdeep Bohra

    (GGSIPU
    Maharaja Surajmal Institute of Technology)

  • Vishal Bhatnagar

    (NSUT (Formally Ambedkar Institute Of Advanced Communication Technologies And Research))

Abstract

Social media has become a tremendous source to bring in new clients. Sharing posts for new offers/products to get extensive client engagement can be predicted by grouping the users based on their previous interactions. In this paper, we improve existing state-of-the-art techniques to predict group-level popularity by extending the data clustering approach and constraint network prediction using stochastic Adam optimization. Various other topological properties of this two-level approach are also tested. The Adam optimization for clustered group prediction improves the relative error substantially. Overall, the proposed novel approach improved the prediction popularity accuracy by a significant difference of 18.21%.

Suggested Citation

  • Navdeep Bohra & Vishal Bhatnagar, 2021. "Group level social media popularity prediction by MRGB and Adam optimization," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 328-347, February.
    • Handle: RePEc:spr:jcomop:v:41:y:2021:i:2:d:10.1007_s10878-020-00684-z
    DOI: 10.1007/s10878-020-00684-z
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    References listed on IDEAS

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    1. Moro, Sérgio & Rita, Paulo & Vala, Bernardo, 2016. "Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach," Journal of Business Research, Elsevier, vol. 69(9), pages 3341-3351.
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